1. Field of the Invention
The present invention relates generally to a data mining method and system and, more specifically, to a distributed agent-based data mining method and system for use in distributed data environments.
2. Description of the Related Art
As the 21st Century begins, we have seen an explosive growth in capabilities to both generate and collect data. It has been estimated that the amount of data in the world doubles every 20 months and the size and number of databases are increasing even faster. Advances in sensor based data collection as well as the computerization of many area of human activities have flooded decision makers with information and generated an urgent need for new techniques and tools that can intelligently and automatically assist them in transforming this huge amount of data into a useful operational and tactical knowledge.
It is recognized that information is at the heart of business operations and that decision-makers should make the best use of data to gain valuable insight into the business.
Current database management systems give access to the data stored but this is only a small part of what could be gained from the data. Traditional on-line transaction processing systems, On-Line Transaction Processes (OLTPs), are good at putting data into databases quickly, safely and efficiently but are not good at delivering meaningful analysis in return. Analyzing data can provide further knowledge about a business by going beyond the data explicitly stored to derive knowledge about the business. This is where Data Mining or Knowledge Discovery in Databases (KDD) has obvious benefits for any enterprise.
Data Mining, or Knowledge Discovery in Databases (KDD) as it is also known, is the nontrivial extraction of implicit, previously unknown, and potentially useful information from data. This encompasses a number of different technical approaches, such as clustering, data summarization, learning classification rules, finding dependency net works, analyzing changes, and detecting anomalies.